{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97982f4b-b3d0-47fe-b6e8-a1a327d03d93",
   "metadata": {},
   "source": [
    "## 深入浅出pandas-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "834d7d19-4015-4048-a1a6-2b8c756f0115",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56becc00-f1c7-4f81-86d1-7f7b23fd65d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format = 'svg'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81bfe94-906c-4e13-b321-0e6c397aea46",
   "metadata": {},
   "source": [
    "### 数据透视\n",
    "\n",
    "1. 数据聚合（指标统计）\n",
    "2. 排序和头部值\n",
    "3. 透视表和交叉表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7836ce8b-1e1d-40e5-b01c-c228ebf60928",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售区域</th>\n",
       "      <th>销售渠道</th>\n",
       "      <th>销售订单</th>\n",
       "      <th>品牌</th>\n",
       "      <th>售价</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>直接成本</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>拼多多</td>\n",
       "      <td>182894-455</td>\n",
       "      <td>八匹马</td>\n",
       "      <td>99</td>\n",
       "      <td>83</td>\n",
       "      <td>3351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>抖音</td>\n",
       "      <td>205635-402</td>\n",
       "      <td>八匹马</td>\n",
       "      <td>219</td>\n",
       "      <td>29</td>\n",
       "      <td>1016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>205654-021</td>\n",
       "      <td>八匹马</td>\n",
       "      <td>169</td>\n",
       "      <td>85</td>\n",
       "      <td>6320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>205654-519</td>\n",
       "      <td>八匹马</td>\n",
       "      <td>169</td>\n",
       "      <td>14</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>377781-010</td>\n",
       "      <td>皮皮虾</td>\n",
       "      <td>249</td>\n",
       "      <td>61</td>\n",
       "      <td>2452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        销售日期 销售区域 销售渠道        销售订单   品牌   售价  销售数量  直接成本\n",
       "0 2020-01-01   上海  拼多多  182894-455  八匹马   99    83  3351\n",
       "1 2020-01-01   上海   抖音  205635-402  八匹马  219    29  1016\n",
       "2 2020-01-01   上海   天猫  205654-021  八匹马  169    85  6320\n",
       "3 2020-01-01   上海   天猫  205654-519  八匹马  169    14   485\n",
       "4 2020-01-01   上海   天猫  377781-010  皮皮虾  249    61  2452"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_df = pd.read_excel('res/2020年销售数据.xlsx', sheet_name='data')\n",
    "sales_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca3b2020-9470-4e49-a3ca-066dc9a437b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1945 entries, 0 to 1944\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   销售日期    1945 non-null   datetime64[ns]\n",
      " 1   销售区域    1945 non-null   object        \n",
      " 2   销售渠道    1945 non-null   object        \n",
      " 3   销售订单    1945 non-null   object        \n",
      " 4   品牌      1945 non-null   object        \n",
      " 5   售价      1945 non-null   int64         \n",
      " 6   销售数量    1945 non-null   int64         \n",
      " 7   直接成本    1945 non-null   int64         \n",
      "dtypes: datetime64[ns](1), int64(3), object(4)\n",
      "memory usage: 121.7+ KB\n"
     ]
    }
   ],
   "source": [
    "sales_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "224ae45b-172a-48bc-8df0-7d6c7099529a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>销售日期</th>\n",
       "      <th>销售区域</th>\n",
       "      <th>销售渠道</th>\n",
       "      <th>销售订单</th>\n",
       "      <th>品牌</th>\n",
       "      <th>售价</th>\n",
       "      <th>销售数量</th>\n",
       "      <th>直接成本</th>\n",
       "      <th>销售额</th>\n",
       "      <th>毛利润</th>\n",
       "      <th>月份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
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       "      <td>拼多多</td>\n",
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       "      <td>八匹马</td>\n",
       "      <td>99</td>\n",
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       "      <td>八匹马</td>\n",
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       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>205654-021</td>\n",
       "      <td>八匹马</td>\n",
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       "      <td>85</td>\n",
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       "      <td>8045</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>205654-519</td>\n",
       "      <td>八匹马</td>\n",
       "      <td>169</td>\n",
       "      <td>14</td>\n",
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       "      <td>1881</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>上海</td>\n",
       "      <td>天猫</td>\n",
       "      <td>377781-010</td>\n",
       "      <td>皮皮虾</td>\n",
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       "      <td>2452</td>\n",
       "      <td>15189</td>\n",
       "      <td>12737</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        销售日期 销售区域 销售渠道        销售订单   品牌   售价  销售数量  直接成本    销售额    毛利润  月份\n",
       "0 2020-01-01   上海  拼多多  182894-455  八匹马   99    83  3351   8217   4866   1\n",
       "1 2020-01-01   上海   抖音  205635-402  八匹马  219    29  1016   6351   5335   1\n",
       "2 2020-01-01   上海   天猫  205654-021  八匹马  169    85  6320  14365   8045   1\n",
       "3 2020-01-01   上海   天猫  205654-519  八匹马  169    14   485   2366   1881   1\n",
       "4 2020-01-01   上海   天猫  377781-010  皮皮虾  249    61  2452  15189  12737   1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 添加销售额、毛利润、月份列\n",
    "sales_df['销售额'] = sales_df.售价 * sales_df.销售数量\n",
    "sales_df['毛利润'] = sales_df.销售额 - sales_df.直接成本\n",
    "sales_df['月份'] = sales_df.销售日期.dt.month\n",
    "sales_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c8878bfd-64de-4a6a-9ae3-36e9af40d45e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_tag(price):\n",
    "    if price < 300:\n",
    "        return '低端'\n",
    "    elif price < 800:\n",
    "        return '中端'\n",
    "    return '高端'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4cba4262-b952-4fce-9b07-fed9c5f2a2e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>销售日期</th>\n",
       "      <th>销售区域</th>\n",
       "      <th>销售渠道</th>\n",
       "      <th>销售订单</th>\n",
       "      <th>品牌</th>\n",
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       "      <th>销售数量</th>\n",
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       "      <th>销售额</th>\n",
       "      <th>毛利润</th>\n",
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       "      <th>价位</th>\n",
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       "      <td>2020-01-01</td>\n",
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       "      <td>天猫</td>\n",
       "      <td>377781-010</td>\n",
       "      <td>皮皮虾</td>\n",
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       "      <td>61</td>\n",
       "      <td>2452</td>\n",
       "      <td>15189</td>\n",
       "      <td>12737</td>\n",
       "      <td>1</td>\n",
       "      <td>低端</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        销售日期 销售区域 销售渠道        销售订单   品牌   售价  销售数量  直接成本    销售额    毛利润  月份  价位\n",
       "0 2020-01-01   上海  拼多多  182894-455  八匹马   99    83  3351   8217   4866   1  低端\n",
       "1 2020-01-01   上海   抖音  205635-402  八匹马  219    29  1016   6351   5335   1  低端\n",
       "2 2020-01-01   上海   天猫  205654-021  八匹马  169    85  6320  14365   8045   1  低端\n",
       "3 2020-01-01   上海   天猫  205654-519  八匹马  169    14   485   2366   1881   1  低端\n",
       "4 2020-01-01   上海   天猫  377781-010  皮皮虾  249    61  2452  15189  12737   1  低端"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根据商品的价格添加价位标签\n",
    "sales_df['价位'] = sales_df.售价.apply(make_tag)\n",
    "sales_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b2a2b472-55fc-4d35-8530-445e8ffbf800",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "销售额: 39772087元\n",
      "毛利润: 27926715元\n",
      "销售数量: 107403件\n",
      "毛利率: 70.22%\n"
     ]
    }
   ],
   "source": [
    "# 统计北极星指标\n",
    "GMV, profit, quantity = sales_df[['销售额', '毛利润', '销售数量']].sum()\n",
    "print(f'销售额: {GMV}元')\n",
    "print(f'毛利润: {profit}元')\n",
    "print(f'销售数量: {quantity}件')\n",
    "print(f'毛利率: {profit / GMV:.2%}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5b6ac225-6834-462f-a5b7-8be8297b66d0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3190039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4164972</td>\n",
       "      <td>2934584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3996770</td>\n",
       "      <td>2882592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3239005</td>\n",
       "      <td>2272902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2817936</td>\n",
       "      <td>1897643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3501304</td>\n",
       "      <td>2449313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2948189</td>\n",
       "      <td>2071782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2632960</td>\n",
       "      <td>1849277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2375385</td>\n",
       "      <td>1671442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2385283</td>\n",
       "      <td>1691646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1691973</td>\n",
       "      <td>1170333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        销售额      毛利润\n",
       "月份                  \n",
       "1   5409855  3845162\n",
       "2   4608455  3190039\n",
       "3   4164972  2934584\n",
       "4   3996770  2882592\n",
       "5   3239005  2272902\n",
       "6   2817936  1897643\n",
       "7   3501304  2449313\n",
       "8   2948189  2071782\n",
       "9   2632960  1849277\n",
       "10  2375385  1671442\n",
       "11  2385283  1691646\n",
       "12  1691973  1170333"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每个月的销售额和毛利润\n",
    "temp1 = sales_df.groupby('月份')[['销售额', '毛利润']].agg('sum')\n",
    "temp1"
   ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>毛利润</th>\n",
       "      <th>销售额</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>月份</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>3845162</td>\n",
       "      <td>5409855</td>\n",
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       "      <th>2</th>\n",
       "      <td>3190039</td>\n",
       "      <td>4608455</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2934584</td>\n",
       "      <td>4164972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2882592</td>\n",
       "      <td>3996770</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2272902</td>\n",
       "      <td>3239005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1897643</td>\n",
       "      <td>2817936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2449313</td>\n",
       "      <td>3501304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2071782</td>\n",
       "      <td>2948189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1849277</td>\n",
       "      <td>2632960</td>\n",
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       "      <th>12</th>\n",
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       "      <td>1691973</td>\n",
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      "text/plain": [
       "        毛利润      销售额\n",
       "月份                  \n",
       "1   3845162  5409855\n",
       "2   3190039  4608455\n",
       "3   2934584  4164972\n",
       "4   2882592  3996770\n",
       "5   2272902  3239005\n",
       "6   1897643  2817936\n",
       "7   2449313  3501304\n",
       "8   2071782  2948189\n",
       "9   1849277  2632960\n",
       "10  1671442  2375385\n",
       "11  1691646  2385283\n",
       "12  1170333  1691973"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "# 使用透视表统计每个月的销售额和毛利润\n",
    "pd.pivot_table(\n",
    "    sales_df,\n",
    "    index='月份',\n",
    "    values=['销售额', '毛利润'],\n",
    "    aggfunc='sum'\n",
    ")"
   ]
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   "id": "3a3cb912-c47f-4d47-8bd4-ffc9eb171cba",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_23\">\n",
       "     <!-- 销售额 -->\n",
       "     <g transform=\"translate(551.496875 47.825) scale(0.1 -0.1)\">\n",
       "      <use xlink:href=\"#SimHei-9500\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "    <g id=\"text_24\">\n",
       "     <!-- 毛利润 -->\n",
       "     <g transform=\"translate(551.496875 61.809375) scale(0.1 -0.1)\">\n",
       "      <use xlink:href=\"#SimHei-6bdb\"/>\n",
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       "     </g>\n",
       "    </g>\n",
       "   </g>\n",
       "  </g>\n",
       " </g>\n",
       " <defs>\n",
       "  <clipPath id=\"p3e3fc2bd2b\">\n",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制折线图\n",
    "temp1.plot(\n",
    "    kind='line',\n",
    "    figsize=(10, 5),\n",
    "    y=['销售额', '毛利润'],   # 放到纵轴上的数据\n",
    "    xlabel='',              # 横轴的标签\n",
    "    ylabel='销售额和毛利润',  # 纵轴的标签\n",
    "    marker='^',             # 标记点符号\n",
    ")\n",
    "# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 >= 3e6, facecolor='red', alpha=0.25)\n",
    "# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 < 3e6, facecolor='green', alpha=0.25)\n",
    "# 定制纵轴的取值范围\n",
    "plt.ylim(0, 6e6)\n",
    "# 定制横轴的刻度\n",
    "plt.xticks(np.arange(1, 13), labels=[f'{x}月' for x in range(1, 13)])\n",
    "# 定制标题\n",
    "plt.title('2020年月度销售额和毛利润', fontdict={'fontsize': 22, 'color': 'navy'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "389db27d-2b11-47ea-a8c6-660fcc39b863",
   "metadata": {},
   "outputs": [
    {
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      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>RdYlBu_r</strong> </div><div class=\"cmap\"><img alt=\"RdYlBu_r colormap\" title=\"RdYlBu_r\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#313695ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #313695ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#a50026ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #a50026ff;\"></div></div></div>"
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      "text/plain": [
       "<matplotlib.colors.LinearSegmentedColormap at 0x1202a1790>"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.cm.RdYlBu_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "621a2487-2d15-450d-9f69-df8089616bd9",
   "metadata": {},
   "outputs": [
    {
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       "<table id=\"T_29991\">\n",
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <th id=\"T_29991_level0_col1\" class=\"col_heading level0 col1\" >销售额月环比</th>\n",
       "      <th id=\"T_29991_level0_col2\" class=\"col_heading level0 col2\" >毛利润</th>\n",
       "      <th id=\"T_29991_level0_col3\" class=\"col_heading level0 col3\" >毛利润月环比</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >月份</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_29991_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_29991_row0_col0\" class=\"data row0 col0\" >5409855</td>\n",
       "      <td id=\"T_29991_row0_col1\" class=\"data row0 col1\" >-------</td>\n",
       "      <td id=\"T_29991_row0_col2\" class=\"data row0 col2\" >3845162</td>\n",
       "      <td id=\"T_29991_row0_col3\" class=\"data row0 col3\" >-------</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_29991_row1_col0\" class=\"data row1 col0\" >4608455</td>\n",
       "      <td id=\"T_29991_row1_col1\" class=\"data row1 col1\" >-14.81%</td>\n",
       "      <td id=\"T_29991_row1_col2\" class=\"data row1 col2\" >3190039</td>\n",
       "      <td id=\"T_29991_row1_col3\" class=\"data row1 col3\" >-17.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_29991_row2_col0\" class=\"data row2 col0\" >4164972</td>\n",
       "      <td id=\"T_29991_row2_col1\" class=\"data row2 col1\" >-9.62%</td>\n",
       "      <td id=\"T_29991_row2_col2\" class=\"data row2 col2\" >2934584</td>\n",
       "      <td id=\"T_29991_row2_col3\" class=\"data row2 col3\" >-8.01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
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       "      <td id=\"T_29991_row3_col1\" class=\"data row3 col1\" >-4.04%</td>\n",
       "      <td id=\"T_29991_row3_col2\" class=\"data row3 col2\" >2882592</td>\n",
       "      <td id=\"T_29991_row3_col3\" class=\"data row3 col3\" >-1.77%</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_29991_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
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       "      <td id=\"T_29991_row4_col2\" class=\"data row4 col2\" >2272902</td>\n",
       "      <td id=\"T_29991_row4_col3\" class=\"data row4 col3\" >-21.15%</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_29991_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_29991_row5_col0\" class=\"data row5 col0\" >2817936</td>\n",
       "      <td id=\"T_29991_row5_col1\" class=\"data row5 col1\" >-13.00%</td>\n",
       "      <td id=\"T_29991_row5_col2\" class=\"data row5 col2\" >1897643</td>\n",
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       "      <th id=\"T_29991_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
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       "      <td id=\"T_29991_row6_col1\" class=\"data row6 col1\" >24.25%</td>\n",
       "      <td id=\"T_29991_row6_col2\" class=\"data row6 col2\" >2449313</td>\n",
       "      <td id=\"T_29991_row6_col3\" class=\"data row6 col3\" >29.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_29991_row7_col0\" class=\"data row7 col0\" >2948189</td>\n",
       "      <td id=\"T_29991_row7_col1\" class=\"data row7 col1\" >-15.80%</td>\n",
       "      <td id=\"T_29991_row7_col2\" class=\"data row7 col2\" >2071782</td>\n",
       "      <td id=\"T_29991_row7_col3\" class=\"data row7 col3\" >-15.41%</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_29991_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_29991_row8_col0\" class=\"data row8 col0\" >2632960</td>\n",
       "      <td id=\"T_29991_row8_col1\" class=\"data row8 col1\" >-10.69%</td>\n",
       "      <td id=\"T_29991_row8_col2\" class=\"data row8 col2\" >1849277</td>\n",
       "      <td id=\"T_29991_row8_col3\" class=\"data row8 col3\" >-10.74%</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_29991_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_29991_row9_col0\" class=\"data row9 col0\" >2375385</td>\n",
       "      <td id=\"T_29991_row9_col1\" class=\"data row9 col1\" >-9.78%</td>\n",
       "      <td id=\"T_29991_row9_col2\" class=\"data row9 col2\" >1671442</td>\n",
       "      <td id=\"T_29991_row9_col3\" class=\"data row9 col3\" >-9.62%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_29991_row10_col0\" class=\"data row10 col0\" >2385283</td>\n",
       "      <td id=\"T_29991_row10_col1\" class=\"data row10 col1\" >0.42%</td>\n",
       "      <td id=\"T_29991_row10_col2\" class=\"data row10 col2\" >1691646</td>\n",
       "      <td id=\"T_29991_row10_col3\" class=\"data row10 col3\" >1.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_29991_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_29991_row11_col0\" class=\"data row11 col0\" >1691973</td>\n",
       "      <td id=\"T_29991_row11_col1\" class=\"data row11 col1\" >-29.07%</td>\n",
       "      <td id=\"T_29991_row11_col2\" class=\"data row11 col2\" >1170333</td>\n",
       "      <td id=\"T_29991_row11_col3\" class=\"data row11 col3\" >-30.82%</td>\n",
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       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x126273190>"
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     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# 计算月环比\n",
    "temp1['销售额月环比'] = temp1.销售额.pct_change()\n",
    "temp1['毛利润月环比'] = temp1.毛利润.pct_change()\n",
    "# 索引重排序\n",
    "temp1 = temp1.reindex(columns=['销售额', '销售额月环比', '毛利润', '毛利润月环比'])\n",
    "# 渲染输出\n",
    "temp1.style.format(\n",
    "    formatter={\n",
    "        '销售额月环比': '{:.2%}',\n",
    "        '毛利润月环比': '{:.2%}'\n",
    "    },\n",
    "    na_rep='-------'\n",
    ").background_gradient(\n",
    "    'RdYlBu_r',\n",
    "    subset=['销售额月环比', '毛利润月环比']\n",
    ")"
   ]
  },
  {
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   "execution_count": 14,
   "id": "6b158aea-fe6e-4938-a4f8-4b880c6f23d0",
   "metadata": {},
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   "source": [
    "# 绘制横线图\n",
    "mu = temp1.销售额.mean()\n",
    "temp1['diff'] = temp1.销售额 - mu\n",
    "temp1['colors'] = temp1.销售额.map(lambda x: 'green' if x > mu else 'red')\n",
    "\n",
    "plt.figure(figsize=(8, 6), dpi=200)\n",
    "plt.hlines(y=temp1.index, xmin=0, xmax=temp1['diff'], color=temp1.colors, alpha=0.6, linewidth=6)\n",
    "plt.yticks(np.arange(1, 13), labels=[f'{x}月' for x in np.arange(1, 13)])\n",
    "# 定制网格线\n",
    "plt.grid(linestyle='--', linewidth=0.4, alpha=0.5)\n",
    "plt.show()"
   ]
  },
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   "execution_count": 15,
   "id": "810936a1-f6c5-43e9-9eda-b9fe8014ba9f",
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       "<Figure size 640x480 with 1 Axes>"
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   "source": [
    "# 各品牌对销售额贡献占比\n",
    "temp2 = sales_df.groupby('品牌')['销售额'].sum()\n",
    "temp2.plot(\n",
    "    kind='pie',\n",
    "    ylabel='',\n",
    "    autopct='%.2f%%',  # 自动计算并显示百分比\n",
    "    pctdistance=0.82,  # 百分比标签到圆心的距离\n",
    "    wedgeprops=dict(width=0.35, edgecolor='w'),  # 定制环状饼图\n",
    "    explode=[0.1, 0, 0, 0, 0],  # 分离饼图\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1465d545-14e8-435d-9b12-651df3bab9ea",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>月份</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>销售区域</th>\n",
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       "      <th>上海</th>\n",
       "      <td>1679125</td>\n",
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       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>1878234</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "月份         1        2        3        4        5        6        7       8   \\\n",
       "销售区域                                                                          \n",
       "上海    1679125  1689527  1061193  1082187   841199   785404   863906  734937   \n",
       "北京    1878234  1807787  1360666  1205989   807300  1216432  1219083  645727   \n",
       "安徽          0        0        0   341308   554155        0        0       0   \n",
       "广东          0        0   388180        0        0        0        0  469390   \n",
       "江苏          0        0        0   537079        0        0   841032       0   \n",
       "浙江          0        0   248354        0        0        0        0  439508   \n",
       "福建    1852496  1111141  1106579   830207  1036351   816100   577283  658627   \n",
       "\n",
       "月份         9       10      11      12  \n",
       "销售区域                                   \n",
       "上海    1107693  412108  825169  528041  \n",
       "北京     390077  671608  678668  596146  \n",
       "安徽          0       0       0       0  \n",
       "广东     365191       0  395188       0  \n",
       "江苏          0  710962       0  215307  \n",
       "浙江          0       0       0       0  \n",
       "福建     769999  580707  486258  352479  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 各销售区域每个月的销售额\n",
    "temp3 = sales_df.groupby(['销售区域', '月份'], as_index=False)[['销售额']].sum()\n",
    "# pivot - 将行旋转到列上（窄表 ----> 宽表）\n",
    "# melt - 将列旋转到行上（宽表 ----> 窄表）\n",
    "temp3.pivot(index='销售区域', columns='月份', values='销售额').fillna(0).astype('i8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1adf23bb-7120-416c-846e-1098aac2e1f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>月份</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>总计</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>销售区域</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>1679125</td>\n",
       "      <td>1689527</td>\n",
       "      <td>1061193</td>\n",
       "      <td>1082187</td>\n",
       "      <td>841199</td>\n",
       "      <td>785404</td>\n",
       "      <td>863906</td>\n",
       "      <td>734937</td>\n",
       "      <td>1107693</td>\n",
       "      <td>412108</td>\n",
       "      <td>825169</td>\n",
       "      <td>528041</td>\n",
       "      <td>11610489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>1878234</td>\n",
       "      <td>1807787</td>\n",
       "      <td>1360666</td>\n",
       "      <td>1205989</td>\n",
       "      <td>807300</td>\n",
       "      <td>1216432</td>\n",
       "      <td>1219083</td>\n",
       "      <td>645727</td>\n",
       "      <td>390077</td>\n",
       "      <td>671608</td>\n",
       "      <td>678668</td>\n",
       "      <td>596146</td>\n",
       "      <td>12477717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>341308</td>\n",
       "      <td>554155</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>895463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>388180</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>469390</td>\n",
       "      <td>365191</td>\n",
       "      <td>0</td>\n",
       "      <td>395188</td>\n",
       "      <td>0</td>\n",
       "      <td>1617949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>537079</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>841032</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>710962</td>\n",
       "      <td>0</td>\n",
       "      <td>215307</td>\n",
       "      <td>2304380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>248354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>439508</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>687862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建</th>\n",
       "      <td>1852496</td>\n",
       "      <td>1111141</td>\n",
       "      <td>1106579</td>\n",
       "      <td>830207</td>\n",
       "      <td>1036351</td>\n",
       "      <td>816100</td>\n",
       "      <td>577283</td>\n",
       "      <td>658627</td>\n",
       "      <td>769999</td>\n",
       "      <td>580707</td>\n",
       "      <td>486258</td>\n",
       "      <td>352479</td>\n",
       "      <td>10178227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>总计</th>\n",
       "      <td>5409855</td>\n",
       "      <td>4608455</td>\n",
       "      <td>4164972</td>\n",
       "      <td>3996770</td>\n",
       "      <td>3239005</td>\n",
       "      <td>2817936</td>\n",
       "      <td>3501304</td>\n",
       "      <td>2948189</td>\n",
       "      <td>2632960</td>\n",
       "      <td>2375385</td>\n",
       "      <td>2385283</td>\n",
       "      <td>1691973</td>\n",
       "      <td>39772087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "月份          1        2        3        4        5        6        7        8  \\\n",
       "销售区域                                                                           \n",
       "上海    1679125  1689527  1061193  1082187   841199   785404   863906   734937   \n",
       "北京    1878234  1807787  1360666  1205989   807300  1216432  1219083   645727   \n",
       "安徽          0        0        0   341308   554155        0        0        0   \n",
       "广东          0        0   388180        0        0        0        0   469390   \n",
       "江苏          0        0        0   537079        0        0   841032        0   \n",
       "浙江          0        0   248354        0        0        0        0   439508   \n",
       "福建    1852496  1111141  1106579   830207  1036351   816100   577283   658627   \n",
       "总计    5409855  4608455  4164972  3996770  3239005  2817936  3501304  2948189   \n",
       "\n",
       "月份          9       10       11       12        总计  \n",
       "销售区域                                                \n",
       "上海    1107693   412108   825169   528041  11610489  \n",
       "北京     390077   671608   678668   596146  12477717  \n",
       "安徽          0        0        0        0    895463  \n",
       "广东     365191        0   395188        0   1617949  \n",
       "江苏          0   710962        0   215307   2304380  \n",
       "浙江          0        0        0        0    687862  \n",
       "福建     769999   580707   486258   352479  10178227  \n",
       "总计    2632960  2375385  2385283  1691973  39772087  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建透视表\n",
    "pd.pivot_table(\n",
    "    sales_df,\n",
    "    index='销售区域',\n",
    "    columns='月份',\n",
    "    values='销售额',\n",
    "    aggfunc='sum',\n",
    "    fill_value=0,\n",
    "    margins=True,\n",
    "    margins_name='总计'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fdf54c9a-64c3-453d-bc5b-533fce7b25ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1945 entries, 0 to 1944\n",
      "Data columns (total 12 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   销售日期    1945 non-null   datetime64[ns]\n",
      " 1   销售区域    1945 non-null   object        \n",
      " 2   销售渠道    1945 non-null   object        \n",
      " 3   销售订单    1945 non-null   object        \n",
      " 4   品牌      1945 non-null   object        \n",
      " 5   售价      1945 non-null   int64         \n",
      " 6   销售数量    1945 non-null   int64         \n",
      " 7   直接成本    1945 non-null   int64         \n",
      " 8   销售额     1945 non-null   int64         \n",
      " 9   毛利润     1945 non-null   int64         \n",
      " 10  月份      1945 non-null   int32         \n",
      " 11  价位      1945 non-null   category      \n",
      "dtypes: category(1), datetime64[ns](1), int32(1), int64(5), object(4)\n",
      "memory usage: 161.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 将价位字段处理成category类型并指定排序的顺序\n",
    "sales_df['价位'] = sales_df.价位.astype('category').cat.reorder_categories(['高端', '中端', '低端'])\n",
    "sales_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e706575-3260-4b37-99fe-696db4f6fe7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>月份</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价位</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>高端</th>\n",
       "      <td>1157</td>\n",
       "      <td>897</td>\n",
       "      <td>642</td>\n",
       "      <td>685</td>\n",
       "      <td>490</td>\n",
       "      <td>378</td>\n",
       "      <td>541</td>\n",
       "      <td>528</td>\n",
       "      <td>210</td>\n",
       "      <td>113</td>\n",
       "      <td>93</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中端</th>\n",
       "      <td>6518</td>\n",
       "      <td>4963</td>\n",
       "      <td>5469</td>\n",
       "      <td>5155</td>\n",
       "      <td>4170</td>\n",
       "      <td>3679</td>\n",
       "      <td>4361</td>\n",
       "      <td>3680</td>\n",
       "      <td>3412</td>\n",
       "      <td>3104</td>\n",
       "      <td>3225</td>\n",
       "      <td>2048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>低端</th>\n",
       "      <td>5220</td>\n",
       "      <td>5525</td>\n",
       "      <td>5127</td>\n",
       "      <td>4430</td>\n",
       "      <td>4625</td>\n",
       "      <td>3947</td>\n",
       "      <td>4184</td>\n",
       "      <td>3233</td>\n",
       "      <td>3618</td>\n",
       "      <td>4008</td>\n",
       "      <td>4199</td>\n",
       "      <td>3642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "月份    1     2     3     4     5     6     7     8     9     10    11    12\n",
       "价位                                                                        \n",
       "高端  1157   897   642   685   490   378   541   528   210   113    93   127\n",
       "中端  6518  4963  5469  5155  4170  3679  4361  3680  3412  3104  3225  2048\n",
       "低端  5220  5525  5127  4430  4625  3947  4184  3233  3618  4008  4199  3642"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每个月各种价位产品的销量\n",
    "temp4 = sales_df.pivot_table(\n",
    "    index='价位',\n",
    "    columns='月份',\n",
    "    values='销售数量',\n",
    "    observed=False,\n",
    "    fill_value=0,\n",
    "    aggfunc='sum'\n",
    ")\n",
    "temp4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "12527025-64f5-4a0a-9bf5-6bdbc4b6f93b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>月份</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>价位</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>高端</th>\n",
       "      <td>1157</td>\n",
       "      <td>897</td>\n",
       "      <td>642</td>\n",
       "      <td>685</td>\n",
       "      <td>490</td>\n",
       "      <td>378</td>\n",
       "      <td>541</td>\n",
       "      <td>528</td>\n",
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       "      <td>113</td>\n",
       "      <td>93</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>中端</th>\n",
       "      <td>6518</td>\n",
       "      <td>4963</td>\n",
       "      <td>5469</td>\n",
       "      <td>5155</td>\n",
       "      <td>4170</td>\n",
       "      <td>3679</td>\n",
       "      <td>4361</td>\n",
       "      <td>3680</td>\n",
       "      <td>3412</td>\n",
       "      <td>3104</td>\n",
       "      <td>3225</td>\n",
       "      <td>2048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>低端</th>\n",
       "      <td>5220</td>\n",
       "      <td>5525</td>\n",
       "      <td>5127</td>\n",
       "      <td>4430</td>\n",
       "      <td>4625</td>\n",
       "      <td>3947</td>\n",
       "      <td>4184</td>\n",
       "      <td>3233</td>\n",
       "      <td>3618</td>\n",
       "      <td>4008</td>\n",
       "      <td>4199</td>\n",
       "      <td>3642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "月份    1     2     3     4     5     6     7     8     9     10    11    12\n",
       "价位                                                                        \n",
       "高端  1157   897   642   685   490   378   541   528   210   113    93   127\n",
       "中端  6518  4963  5469  5155  4170  3679  4361  3680  3412  3104  3225  2048\n",
       "低端  5220  5525  5127  4430  4625  3947  4184  3233  3618  4008  4199  3642"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 交叉表\n",
    "pd.crosstab(\n",
    "    index=sales_df.价位,\n",
    "    columns=sales_df.月份,\n",
    "    values=sales_df.销售数量,\n",
    "    aggfunc='sum'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d99ecd49-2669-493c-ae04-974afa71dc4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>人格</th>\n",
       "      <th>𝛂</th>\n",
       "      <th>𝛃</th>\n",
       "      <th>𝛄</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>血型</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "人格  𝛂  𝛃  𝛄\n",
       "血型         \n",
       "A   2  1  1\n",
       "AB  1  1  0\n",
       "B   4  1  1\n",
       "O   5  0  3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "blood_types = np.array(['B', 'A', 'O', 'O', 'AB', 'B', 'O', 'B', 'AB', 'A', 'A', 'O', 'B', 'O', 'O', 'O', 'O', 'A', 'B', 'B'])\n",
    "personality_types = np.array(['𝛃', '𝛂', '𝛂', '𝛂', '𝛃', '𝛂', '𝛄', '𝛄', '𝛂', '𝛄', '𝛃', '𝛂', '𝛂', '𝛂', '𝛄', '𝛄', '𝛂', '𝛂', '𝛂', '𝛂'])\n",
    "\n",
    "# 创建交叉表\n",
    "pd.crosstab(\n",
    "    index=blood_types,\n",
    "    columns=personality_types,\n",
    "    rownames=['血型'],\n",
    "    colnames=['人格'],\n",
    ")"
   ]
  },
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   "source": [
    "# 绘制堆叠柱状图\n",
    "temp4.T.plot(\n",
    "    figsize=(10, 4),\n",
    "    kind='bar',\n",
    "    width=0.6,\n",
    "    xlabel='',\n",
    "    ylabel='销售数量',\n",
    "    stacked=True\n",
    ")\n",
    "plt.xticks(rotation=0)\n",
    "plt.show()"
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  {
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   "execution_count": 23,
   "id": "9eba1698-d8a5-4b66-b617-fc68072a670e",
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    {
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       "      <th>价位</th>\n",
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       "      <th>中端</th>\n",
       "      <th>低端</th>\n",
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       "  <tbody>\n",
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       "      <th>1</th>\n",
       "      <td>0.089725</td>\n",
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       "    <tr>\n",
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       "      <td>0.449111</td>\n",
       "      <td>0.498115</td>\n",
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       "      <td>0.494557</td>\n",
       "      <td>0.434485</td>\n",
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       "      <td>0.471271</td>\n",
       "      <td>0.499724</td>\n",
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       "      <td>0.429619</td>\n",
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       "价位        高端        中端        低端\n",
       "月份                              \n",
       "1   0.089725  0.505467  0.404808\n",
       "2   0.078788  0.435924  0.485288\n",
       "3   0.057128  0.486652  0.456220\n",
       "4   0.066699  0.501947  0.431353\n",
       "5   0.052773  0.449111  0.498115\n",
       "6   0.047226  0.459645  0.493128\n",
       "7   0.059542  0.479969  0.460489\n",
       "8   0.070958  0.494557  0.434485\n",
       "9   0.029006  0.471271  0.499724\n",
       "10  0.015640  0.429619  0.554740\n",
       "11  0.012372  0.429028  0.558601\n",
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   "source": [
    "# 让每一项数据除以对应月份的销售数量之和\n",
    "temp5 = temp4.T.divide(temp4.sum(), axis=0)\n",
    "temp5"
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       "    </g>\n",
       "   </g>\n",
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       "   </g>\n",
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       "    </g>\n",
       "   </g>\n",
       "   <g id=\"text_25\">\n",
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       "    </g>\n",
       "   </g>\n",
       "   <g id=\"text_26\">\n",
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       "    <g transform=\"translate(198.777931 164.086248) scale(0.08 -0.08)\">\n",
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   "source": [
    "# 绘制百分比堆叠柱状图\n",
    "temp5.plot(\n",
    "    figsize=(10, 4),\n",
    "    kind='bar',\n",
    "    width=0.6,\n",
    "    xlabel='',\n",
    "    ylabel='销量占比',\n",
    "    stacked=True\n",
    ")\n",
    "plt.xticks(rotation=0)\n",
    "plt.yticks(np.linspace(0, 1, 6), labels=[f'{x:.0%}' for x in np.linspace(0, 1, 6)])\n",
    "plt.legend(loc='lower center')\n",
    "\n",
    "for i in temp5.index:\n",
    "    y1, y2, y3 = temp5.loc[i]\n",
    "    plt.text(i - 1, y2 / 2 + y1, f'{y2:.2%}', ha='center', va='center', fontdict={'size': 8})\n",
    "    plt.text(i - 1, y3 / 2 + y2 + y1, f'{y3:.2%}', ha='center', va='center', fontdict={'size': 8})\n",
    "\n",
    "plt.show()"
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  {
   "cell_type": "markdown",
   "id": "82ac6b37-302e-4913-a973-4a7f4e6daf1d",
   "metadata": {},
   "source": [
    "### 作业：招聘岗位数据分析\n",
    "\n",
    "1. 统计出城市、招聘信息、招聘岗位的数量和平均月薪。\n",
    "2. 统计每个城市的岗位数量从高到低排序。\n",
    "3. 统计每个城市的平均薪资从高到低排序。\n",
    "4. 统计招聘岗位对学历要求的占比。\n",
    "5. 统计招聘岗位对工作年限的要求占比。\n",
    "6. 分析薪资跟学历和工作年限的关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91e8b171-7568-4da1-8170-5d13fc8945bc",
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       "     company_name  salary  year  edu   job_name  pos_count  salary_lower  \\\n",
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       "\n",
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    "jobs_df = pd.read_csv('res/cleaned_jobs.csv')\n",
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  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7cadf547-f7aa-494b-beeb-678f5a09e967",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "城市数量: 9\n",
      "信息数量: 6487\n",
      "岗位数量: 22611\n",
      "平均薪资: 17.0\n"
     ]
    }
   ],
   "source": [
    "# 统计北极星指标\n",
    "city_count = jobs_df['city'].nunique()\n",
    "info_count = jobs_df['company_name'].count()\n",
    "post_count = jobs_df['pos_count'].sum()\n",
    "salary_avg = jobs_df['salary'].mean().round(1)\n",
    "print(f'城市数量: {city_count}')\n",
    "print(f'信息数量: {info_count}')\n",
    "print(f'岗位数量: {post_count}')\n",
    "print(f'平均薪资: {salary_avg}')"
   ]
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   "cell_type": "code",
   "execution_count": 27,
   "id": "cb9fc057-df56-4f01-a08e-ab3427dca467",
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      "text/plain": [
       "      pos_count\n",
       "city           \n",
       "上海         3158\n",
       "深圳         3061\n",
       "北京         3009\n",
       "杭州         2732\n",
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       "南京         2345\n",
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    "# 统计每个城市的岗位数量从高到低排序\n",
    "jobs_df.groupby('city')[['pos_count']].sum().sort_values(by='pos_count', ascending=False)"
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   "execution_count": 28,
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       "上海         3158\n",
       "深圳         3061\n",
       "北京         3009\n",
       "杭州         2732\n",
       "广州         2560\n",
       "南京         2345\n",
       "武汉         2031\n",
       "成都         1913\n",
       "西安         1802"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(\n",
    "    jobs_df,\n",
    "    index='city',\n",
    "    values='pos_count',\n",
    "    aggfunc='sum'\n",
    ").sort_values(by='pos_count', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bcd4aeca-bb90-42e6-b45c-6cdea2c76271",
   "metadata": {},
   "outputs": [
    {
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       "      <th>salary</th>\n",
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       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>23.6</td>\n",
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       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>20.3</td>\n",
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       "    <tr>\n",
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       "      <th>西安</th>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      salary\n",
       "city        \n",
       "北京      23.6\n",
       "深圳      20.3\n",
       "上海      19.8\n",
       "杭州      18.9\n",
       "广州      14.4\n",
       "南京      14.2\n",
       "成都      12.6\n",
       "武汉      11.8\n",
       "西安      10.5"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jobs_df.groupby('city')[['salary']].mean().round(1).sort_values(by='salary', ascending=False)"
   ]
  },
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   "execution_count": 30,
   "id": "6e93f5c8-da3e-4064-ac77-748eb3167504",
   "metadata": {},
   "outputs": [
    {
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       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>深圳</th>\n",
       "      <td>20.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>杭州</th>\n",
       "      <td>18.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广州</th>\n",
       "      <td>14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>南京</th>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>成都</th>\n",
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       "      <th>西安</th>\n",
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      "text/plain": [
       "      salary\n",
       "city        \n",
       "北京      23.6\n",
       "深圳      20.3\n",
       "上海      19.8\n",
       "杭州      18.9\n",
       "广州      14.4\n",
       "南京      14.2\n",
       "成都      12.6\n",
       "武汉      11.8\n",
       "西安      10.5"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计每个城市的平均薪资从高到低排序\n",
    "pd.pivot_table(\n",
    "    jobs_df,\n",
    "    index='city',\n",
    "    values='salary',\n",
    "    aggfunc='mean'\n",
    ").round(1).sort_values(by='salary', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7ae067bf-2981-49f2-a224-28a5223dc21f",
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_df['edu'] = jobs_df.edu.astype('category').cat.reorder_categories(['学历不限', '大专', '本科', '研究生'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a87fab8f-18af-4c0b-9546-63d79778b4d8",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "# 统计招聘岗位对学历要求占比\n",
    "pd.pivot_table(\n",
    "    jobs_df,\n",
    "    index='edu',\n",
    "    values='pos_count',\n",
    "    aggfunc='sum',\n",
    "    observed=True\n",
    ").plot(\n",
    "    kind='pie',\n",
    "    ylabel='',\n",
    "    subplots=True,\n",
    "    legend=False,\n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.85,\n",
    "    wedgeprops={'width': 0.35}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "15202818-48a8-40bb-8701-47bc1b481f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "jobs_df['year'] = jobs_df.year.astype('category').cat.reorder_categories(['应届生', '1年以内', '经验不限', '1-3年', '3-5年', '5年以上'])"
   ]
  },
  {
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   "execution_count": 34,
   "id": "6d965229-36d5-4c38-850c-3300726f208a",
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      ],
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 统计招聘岗位对工作年限要求绘制饼图\n",
    "pd.pivot_table(\n",
    "    jobs_df,\n",
    "    index='year',\n",
    "    values='pos_count',\n",
    "    aggfunc='sum',\n",
    "    observed=True\n",
    ").plot(\n",
    "    kind='pie',\n",
    "    y='pos_count',\n",
    "    ylabel='',\n",
    "    legend=False,\n",
    "    autopct='%.2f%%',\n",
    "    pctdistance=0.85,\n",
    "    wedgeprops={'width': 0.35}\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9c7ab419-0bc4-428f-be9b-6d4d617b6663",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>应届生</th>\n",
       "      <th>1年以内</th>\n",
       "      <th>经验不限</th>\n",
       "      <th>1-3年</th>\n",
       "      <th>3-5年</th>\n",
       "      <th>5年以上</th>\n",
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       "    <tr>\n",
       "      <th>edu</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>学历不限</th>\n",
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       "      <td>8.2</td>\n",
       "      <td>10.9</td>\n",
       "      <td>10.9</td>\n",
       "      <td>16.4</td>\n",
       "      <td>21.1</td>\n",
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       "    <tr>\n",
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       "      <td>7.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>9.4</td>\n",
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       "      <td>22.5</td>\n",
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       "    <tr>\n",
       "      <th>本科</th>\n",
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       "      <td>9.6</td>\n",
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       "    <tr>\n",
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       "      <td>8.3</td>\n",
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       "      <td>24.8</td>\n",
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      "text/plain": [
       "year  应届生  1年以内  经验不限  1-3年  3-5年  5年以上\n",
       "edu                                    \n",
       "学历不限  9.2   8.2  10.9  10.9  16.4  21.1\n",
       "大专    7.2   8.0   8.4   9.4  14.3  22.5\n",
       "本科    7.3   9.6  13.0  13.2  20.0  25.5\n",
       "研究生   8.3  12.7  15.2  17.1  24.8  30.5"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 统计不同学历和工作年限平均薪资\n",
    "temp6 = pd.pivot_table(\n",
    "    jobs_df,\n",
    "    index='edu',\n",
    "    columns='year',\n",
    "    values='salary',\n",
    "    observed=False,\n",
    "    fill_value=0\n",
    ").round(1)\n",
    "temp6"
   ]
  },
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   "execution_count": 36,
   "id": "d8492b17-5ae8-47f0-a058-ab303a6087a9",
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   "source": [
    "# 绘制热力图\n",
    "plt.imshow(temp6, cmap='Reds')\n",
    "plt.xticks(np.arange(6), labels=temp6.columns)\n",
    "plt.yticks(np.arange(4), labels=temp6.index)\n",
    "\n",
    "for i in range(temp6.index.size):\n",
    "    for j in range(temp6.columns.size):\n",
    "        value = temp6.iat[i, j]\n",
    "        color = 'w' if value > salary_avg else 'k'\n",
    "        plt.text(j, i, value, ha='center', va='center', color=color)\n",
    "\n",
    "# 定制颜色条\n",
    "plt.colorbar()\n",
    "plt.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a3eb8cd0-fe07-43a4-9514-8a38c08ca081",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install seaborn"
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f6287994-0865-432a-9932-92b05b5bf7e8",
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    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(temp6, cmap='Reds', annot=True)\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "plt.yticks(rotation=0)\n",
    "plt.show()"
   ]
  }
 ],
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